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Activity Number: 84
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319351
Title: Bayesian Joint Modeling of Response Times with Dynamic Latent Ability in Educational Testing
Author(s): Abhisek Saha* and Xiaojing Wang and Dipak Dey
Companies: University of Connecticut and University of Connecticut and University of Connecticut
Keywords: Computerized Testing ; Dynamic Item Response Models ; Local Dependence ; Longitudinal Observations ; Markov chain Monte Carlo ; Bayesian hierarchical models

In educational testing, inferences on ability have been mainly based on item responses while the time taken to complete an item are often ignored . With the advent of computerized testing, information on response time of each item can be obtained without additional cost. To better infer latent ability, a new class of state space models, conjointly modeling response time with time series of dichotomous responses, is put forward. The proposed models can entertain longitudinal observations at individually-varying and irregularly-spaced time points and can accommodate changes in ability and other complications, such as local dependence and randomized item difficulty. Simulations for the proposed models demonstrate that the biases of ability estimation are reduced and their precisions are improved. In applying the models to a large collection of reading test data from MetaMetrics company, we further investigated two competitive relationship in modeling response times with the distance of ability and item difficulty (i.e.monotone or inverted U-shape). The model comparison result supports the inverted U-shape relationship better captures examinees' behaviors and psychology in exams.

Authors who are presenting talks have a * after their name.

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